def gaussian_draw_test():
import os
import numpy as np
from pyemu import MonteCarlo,Cov,ParameterEnsemble
from datetime import datetime
jco = os.path.join("pst","pest.jcb")
pst = jco.replace(".jcb",".pst")
mc = MonteCarlo(jco=jco,pst=pst)
num_reals = 100
start = datetime.now()
mc.draw(num_reals=num_reals,how="gaussian")
print(mc.parensemble.head())
print(datetime.now() - start)
vals = mc.pst.parameter_data.parval1.values
cov = Cov.from_parameter_data(mc.pst)
start = datetime.now()
val_array = np.random.multivariate_normal(vals, cov.as_2d,num_reals)
print(datetime.now() - start)
start = datetime.now()
pe = ParameterEnsemble.from_gaussian_draw(mc.pst,cov,num_reals=num_reals)
pet = pe._transform(inplace=False)
pe = pet._back_transform(inplace=False)
print(datetime.now() - start)
print(mc.parensemble.head())
print(pe.head())
def parfile_test():
import os
import numpy as np
import pandas as pd
from pyemu import MonteCarlo, Ensemble, ParameterEnsemble, Pst, Cov
jco = os.path.join("pst", "pest.jcb")
pst = jco.replace(".jcb", ".pst")
mc = MonteCarlo(jco=jco, pst=pst)
mc.pst.parameter_data.loc[mc.pst.par_names[1], "scale"] = 0.001
mc.draw(10)
mc.parensemble.to_parfiles(os.path.join("temp", "testpar"))
pst = Pst(pst)
pst.parameter_data = pst.parameter_data.iloc[1:]
pst.parameter_data["test", "parmne"] = "test"
parfiles = [
os.path.join("temp", f) for f in os.listdir("temp") if "testpar" in f
]
rnames = ["test{0}".format(i) for i in range(len(parfiles))]
pe = ParameterEnsemble.from_parfiles(pst=pst,
parfile_names=parfiles,
real_names=rnames)
def fixed_par_test():
import os
import numpy as np
from pyemu import MonteCarlo,ParameterEnsemble
jco = os.path.join("pst","pest.jcb")
pst = jco.replace(".jcb",".pst")
mc = MonteCarlo(jco=jco,pst=pst)
mc.pst.parameter_data.loc["mult1","partrans"] = "fixed"
mc.draw(10)
assert np.all(mc.parensemble.loc[:,"mult1"] ==
mc.pst.parameter_data.loc["mult1","parval1"])
pe = ParameterEnsemble.from_gaussian_draw(mc.pst,mc.parcov,2)
def from_dataframe_test():
import os
import numpy as np
import pandas as pd
from pyemu import MonteCarlo,Ensemble,ParameterEnsemble,Pst, Cov
jco = os.path.join("pst","pest.jcb")
pst = jco.replace(".jcb",".pst")
mc = MonteCarlo(jco=jco,pst=pst)
names = ["par_{0}".format(_) for _ in range(10)]
df = pd.DataFrame(np.random.random((10,mc.pst.npar)),columns=mc.pst.par_names)
mc.parensemble = ParameterEnsemble.from_dataframe(df=df,pst=mc.pst)
print(mc.parensemble.shape)
mc.project_parensemble()
mc.parensemble.to_csv(os.path.join("temp","test.csv"))
pstc = Pst(pst)
par = pstc.parameter_data
par.sort_values(by="parnme",ascending=False,inplace=True)
cov = Cov.from_parameter_data(pstc)
pe = ParameterEnsemble.from_gaussian_draw(pst=mc.pst,cov=cov)
def from_dataframe_test():
import os
import numpy as np
import pandas as pd
from pyemu import MonteCarlo,Ensemble,ParameterEnsemble,Pst
jco = os.path.join("pst","pest.jcb")
pst = jco.replace(".jcb",".pst")
mc = MonteCarlo(jco=jco,pst=pst)
names = ["par_{0}".format(_) for _ in range(10)]
df = pd.DataFrame(np.random.random((10,mc.pst.npar)),columns=mc.pst.par_names)
mc.parensemble = ParameterEnsemble.from_dataframe(df=df,pst=mc.pst)
print(mc.parensemble.shape)
mc.project_parensemble()
mc.parensemble.to_csv(os.path.join("temp","test.csv"))
def from_dataframe_test():
import os
import numpy as np
import pandas as pd
from pyemu import MonteCarlo,Ensemble,ParameterEnsemble,Pst
jco = os.path.join("pst","pest.jcb")
pst = jco.replace(".jcb",".pst")
mc = MonteCarlo(jco=jco,pst=pst)
names = ["par_{0}".format(_) for _ in range(10)]
df = pd.DataFrame(np.random.random((10,mc.pst.npar)),columns=mc.pst.par_names)
mc.parensemble = ParameterEnsemble.from_dataframe(df=df,pst=mc.pst)
print(mc.parensemble.shape)
mc.project_parensemble()
mc.parensemble.to_csv(os.path.join("temp","test.csv"))
def diagonal_cov_draw_test():
import os
import numpy as np
from pyemu import MonteCarlo,Cov,Pst,ParameterEnsemble
jco = os.path.join("pst","pest.jcb")
pst = Pst(jco.replace(".jcb",".pst"))
mc = MonteCarlo(jco=jco,pst=pst)
num_reals = 100
mc.draw(num_reals,obs=True)
print(mc.obsensemble)
pe1 = mc.parensemble.copy()
cov = Cov(x=mc.parcov.as_2d,names=mc.parcov.row_names)
#print(type(cov))
mc = MonteCarlo(jco=jco,pst=pst)
mc.parensemble.reseed()
mc.draw(num_reals,cov=cov)
pe2 = mc.parensemble
pe3 = ParameterEnsemble.from_gaussian_draw(mc.pst,num_reals=num_reals,cov=mc.parcov)
def parfile_test():
import os
import numpy as np
import pandas as pd
from pyemu import MonteCarlo, Ensemble, ParameterEnsemble, Pst, Cov
jco = os.path.join("pst", "pest.jcb")
pst = jco.replace(".jcb", ".pst")
mc = MonteCarlo(jco=jco, pst=pst)
mc.pst.parameter_data.loc[mc.pst.par_names[1], "scale"] = 0.001
mc.draw(10)
mc.parensemble.to_parfiles(os.path.join("temp","testpar"))
pst = Pst(pst)
pst.parameter_data = pst.parameter_data.iloc[1:]
pst.parameter_data["test","parmne"] = "test"
parfiles = [os.path.join("temp",f) for f in os.listdir("temp") if "testpar" in f]
rnames = ["test{0}".format(i) for i in range(len(parfiles))]
pe = ParameterEnsemble.from_parfiles(pst=pst,parfile_names=parfiles,real_names=rnames)